Correct estimation of variogram parameters relies on having a sufficiently large dataset. However, operational agri-datasets are often not large enough for variogram fitting. This article presents a new approach to estimating semi-variogram parameters from a small dataset by using a Bayesian approach. The three variogram parameters of the Spherical-Plus-Nugget model were fitted to the semi-variances of a vineyard water stress indicator. Two sources of prior information (i.e. using ancillary data, and using some simplistic assumptions), and six reduced datasets were tested. The results showed that using prior information introduced less variability in estimation results than with the classical approach. The priors extracted from the Sentinel-2 data significantly improved the estimation of the nugget effect, which allowed better preservation of the spatial pattern of kriging predictions.